This notebook contains a set of analyses for analyzing markbesada’s boardgamegeek collection. The bulk of the analysis is focused on building a user-specific predictive model to predict the games that the specified user is likely to own. This enables us to ask questions like, based on the games the user currently owns, what games are a good fit for their collection? What upcoming games are they likely to purchase?
We can look at a basic description of the number of games that the user owns, has rated, has previously owned, etc.
What years has the user owned/rated games from? While we can’t see when a user added or removed a game from their collection, we can look at their collection by the years in which their games were published.
We can look at the most frequent types of categories, mechanics, designers, and artists that appear in a user’s collection.
We’ll examine predictive models trained on a user’s collection for games published through 2020. How many games has the user owned/rated/played in the training set (games prior to 2020)?
username | dataset | period | games_owned | games_rated |
markbesada | training | published before 2020 | 1,442 | 641 |
markbesada | validation | published 2020 | 116 | 0 |
markbesada | test | published after 2020 | 59 | 0 |
The main outcome we will be modeling for the user is owned, which refers to whether the user currently owns or has a previously owned a game in their collection. Our goal is to train a predictive model to learn the probability that a user will add a game to their collection based on its observable features. This amounts to looking at historical data and looking to find patterns that exist between features of games and games present in the user’s collection.
One of the models we trained was a decision tree, which looks for decision rules that can be used to separate games the user owns from games they don’t. The resulting model produces a decision corresponding to yes or no statements: to explain why the model predicts the user to own game, we start at the top of the tree and follow the rules that were learned from the training data.
Note: the tree below has been further pruned to make it easier to visualize.
Decision trees are highly interpretible models that are easy to train and can identify important interactions and nonlinearities present in the data. Individual trees have the drawback of being less predictive than other common models, but it can be useful to look at them to gain some understanding of key predictors and relationships found in the training data.
We can examine coefficients from another model we trained, which is a logistic regression with elastic net regularization (which I will refer to as a penalized logistic regression). Positive values indicate that a feature increases a user’s probability of owning/rating a game, while negative values indicate a feature decreases the probability. To be precise, the coefficients indicate the effect of a particular feature on the log-odds of a user owning a game.
Why did the model identify these features? We can make density plots of the important features for predicting whether the user owned a game. Blue indicates the density for games owned by the user, while grey indicates the density for games not owned by the user.
Binary predictors can be difficult to see with this visualization, so we can also directly examine the percentage of games in a user’s collection with a predictor vs the percentage of all games with that predictor.
% of Games with Feature | ||||
username | Feature | User_Collection | All_Games | Ratio |
markbesada | Stronghold Games | 3.1% | 0.4% | 7.59 |
markbesada | ZMan Games | 6.0% | 1.1% | 5.49 |
markbesada | Asmodee | 8.9% | 2.2% | 4.12 |
markbesada | Eagle-Gryphon Games | 2.4% | 0.6% | 3.77 |
markbesada | Rio Grande Games | 5.9% | 1.6% | 3.63 |
markbesada | GMT Games | 2.8% | 1.2% | 2.27 |
markbesada | Crowdfunding Kickstarter | 22.9% | 11.8% | 1.93 |
markbesada | Deduction Game | 7.9% | 4.9% | 1.62 |
markbesada | Realtime Game | 5.0% | 3.4% | 1.49 |
markbesada | Ravensburger | 3.4% | 2.4% | 1.41 |
markbesada | Party Game | 10.9% | 9.3% | 1.18 |
markbesada | Dice Rolling | 25.7% | 28.7% | 0.90 |
markbesada | Adventure | 4.5% | 5.1% | 0.88 |
markbesada | Wargame | 10.5% | 19.3% | 0.55 |
markbesada | Collectible Components | 0.4% | 1.9% | 0.22 |
markbesada | Childrens Game | 1.4% | 8.5% | 0.16 |
Before predicting games in upcoming years, we can examine how well the model did and what games it liked in the training set. In this case, we used resampling techniques (cross validation) to ensure that the model had not seen a game before making its predictions.
Displaying the 100 games from the training set with the highest probability of ownership, highlighting in blue games the user has owned.
Rank | Published | ID | Name | Pr(Owned) | Owned |
1 | 2015 | 175878 | 504 | 0.997 | no |
2 | 2013 | 143693 | Glass Road | 0.986 | yes |
3 | 2016 | 167791 | Terraforming Mars | 0.973 | yes |
4 | 2019 | 276025 | Maracaibo | 0.972 | yes |
5 | 2017 | 220308 | Gaia Project | 0.968 | yes |
6 | 2017 | 174430 | Gloomhaven | 0.961 | yes |
7 | 2018 | 205896 | Rising Sun | 0.959 | yes |
8 | 2010 | 73439 | Troyes | 0.957 | yes |
9 | 2016 | 169786 | Scythe | 0.954 | yes |
10 | 2019 | 286096 | Tapestry | 0.952 | no |
11 | 2010 | 70512 | Luna | 0.950 | yes |
12 | 2002 | 4098 | Age of Steam | 0.948 | yes |
13 | 2013 | 124052 | Cinque Terre | 0.945 | yes |
14 | 1999 | 552 | Bus | 0.944 | yes |
15 | 2014 | 145371 | Three Kingdoms Redux | 0.940 | yes |
16 | 2012 | 123096 | Space Cadets | 0.935 | yes |
17 | 2019 | 283863 | The Magnificent | 0.934 | yes |
18 | 2012 | 121921 | Robinson Crusoe: Adventures on the Cursed Island | 0.934 | yes |
19 | 2008 | 35677 | Le Havre | 0.933 | yes |
20 | 2016 | 200680 | Agricola (Revised Edition) | 0.929 | no |
21 | 2012 | 120677 | Terra Mystica | 0.910 | yes |
22 | 2009 | 39683 | At the Gates of Loyang | 0.909 | yes |
23 | 2014 | 146886 | La Granja | 0.908 | yes |
24 | 2008 | 38453 | Space Alert | 0.906 | yes |
25 | 2019 | 270971 | Era: Medieval Age | 0.900 | yes |
26 | 2017 | 162886 | Spirit Island | 0.898 | yes |
27 | 2014 | 164928 | Orléans | 0.896 | yes |
28 | 2012 | 122515 | Keyflower | 0.896 | yes |
29 | 2019 | 266507 | Clank!: Legacy – Acquisitions Incorporated | 0.895 | no |
30 | 2010 | 62219 | Dominant Species | 0.894 | yes |
31 | 2012 | 111341 | The Great Zimbabwe | 0.892 | yes |
32 | 2017 | 195455 | BOX | 0.891 | yes |
33 | 2017 | 233078 | Twilight Imperium: Fourth Edition | 0.889 | no |
34 | 2015 | 158915 | GEM | 0.889 | yes |
35 | 2014 | 159508 | AquaSphere | 0.880 | no |
36 | 2018 | 244711 | Newton | 0.878 | yes |
37 | 2011 | 84876 | The Castles of Burgundy | 0.874 | yes |
38 | 2018 | 199792 | Everdell | 0.873 | yes |
39 | 2007 | 31260 | Agricola | 0.872 | yes |
40 | 2019 | 253635 | Ragusa | 0.865 | yes |
41 | 2018 | 222509 | Lords of Hellas | 0.862 | no |
42 | 2017 | 229265 | Wendake | 0.856 | no |
43 | 2017 | 197178 | DIG | 0.856 | yes |
44 | 2014 | 159675 | Fields of Arle | 0.854 | yes |
45 | 2018 | 235344 | TOKYO METRO | 0.854 | no |
46 | 2010 | 62227 | Labyrinth: The War on Terror, 2001 – ? | 0.852 | yes |
47 | 2016 | 177736 | A Feast for Odin | 0.846 | yes |
48 | 2016 | 176083 | Hit Z Road | 0.845 | no |
49 | 2009 | 40237 | Long Shot | 0.845 | yes |
50 | 2017 | 192827 | RUM | 0.840 | yes |
51 | 2015 | 170216 | Blood Rage | 0.839 | yes |
52 | 2015 | 172386 | Mombasa | 0.839 | yes |
53 | 2002 | 3076 | Puerto Rico | 0.833 | no |
54 | 1997 | 42 | Tigris & Euphrates | 0.829 | yes |
55 | 2017 | 216132 | Clans of Caledonia | 0.825 | yes |
56 | 2018 | 256916 | Concordia Venus | 0.824 | no |
57 | 2019 | 217576 | Hellenica: Story of Greece | 0.823 | no |
58 | 2011 | 70919 | Takenoko | 0.822 | no |
59 | 2008 | 33107 | Senji | 0.811 | no |
60 | 1999 | 204 | Stephenson's Rocket | 0.807 | yes |
61 | 2012 | 113294 | Escape: The Curse of the Temple | 0.807 | yes |
62 | 2017 | 221805 | Breaking Bad: The Board Game | 0.799 | no |
63 | 2004 | 2651 | Power Grid | 0.796 | yes |
64 | 2018 | 248562 | Mage Knight: Ultimate Edition | 0.794 | yes |
65 | 2014 | 157354 | Five Tribes | 0.794 | yes |
66 | 2005 | 17133 | Railways of the World | 0.789 | yes |
67 | 2017 | 192824 | GYM | 0.788 | yes |
68 | 2012 | 117915 | Yedo | 0.786 | yes |
69 | 2017 | 195539 | The Godfather: Corleone's Empire | 0.784 | no |
70 | 2018 | 214887 | CO₂: Second Chance | 0.782 | yes |
71 | 2017 | 195456 | SPY | 0.782 | yes |
72 | 2019 | 256730 | Pipeline | 0.780 | yes |
73 | 2014 | 154203 | Imperial Settlers | 0.778 | no |
74 | 1973 | 8326 | The Fall of Rome | 0.778 | yes |
75 | 2019 | 251247 | Barrage | 0.773 | yes |
76 | 2019 | 272739 | Clinic: Deluxe Edition | 0.772 | yes |
77 | 2019 | 244099 | Herbaceous Sprouts | 0.772 | yes |
78 | 1973 | 2995 | Sniper! | 0.771 | yes |
79 | 2018 | 260428 | Pandemic: Fall of Rome | 0.767 | no |
80 | 2014 | 161882 | Irish Gauge | 0.761 | yes |
81 | 2018 | 170604 | Renegade | 0.761 | yes |
82 | 1999 | 54 | Tikal | 0.756 | yes |
83 | 2013 | 140620 | Lewis & Clark: The Expedition | 0.754 | no |
84 | 2013 | 127024 | Room 25 | 0.752 | no |
85 | 2010 | 65200 | Asteroyds | 0.752 | yes |
86 | 2017 | 161533 | Lisboa | 0.745 | yes |
87 | 2011 | 104006 | Village | 0.744 | no |
88 | 2019 | 265736 | Tiny Towns | 0.742 | yes |
89 | 2019 | 257066 | Sierra West | 0.742 | yes |
90 | 2017 | 192829 | SOW | 0.742 | yes |
91 | 2017 | 188920 | This War of Mine: The Board Game | 0.741 | yes |
92 | 2018 | 247763 | Underwater Cities | 0.740 | yes |
93 | 2013 | 146278 | Tash-Kalar: Arena of Legends | 0.739 | no |
94 | 2017 | 234487 | Altiplano | 0.739 | no |
95 | 2009 | 54998 | Cyclades | 0.736 | yes |
96 | 2010 | 66362 | Glen More | 0.735 | no |
97 | 2015 | 181304 | Mysterium | 0.735 | yes |
98 | 2007 | 28720 | Brass: Lancashire | 0.735 | yes |
99 | 2014 | 163412 | Patchwork | 0.732 | yes |
100 | 2013 | 124361 | Concordia | 0.728 | yes |
This section contains a variety of visualizations and metrics for assessing the performance of the model(s) during resampling. If you’re not particularly interested in predictive modeling, skip down further to the predictions from the model.
An easy way to examine the performance of classification model is to view a separation plot. We plot the predicted probabilities from the model for every game (from resampling) from lowest to highest. We then overlay a blue line for any game that the user does own. A good classifier is one that is able to separate the blue (games owned by the user) from the white (games not owned by the user), with most of the blue occurring at the highest probabilities (right side of the chart).
We can more formally assess how well each model did in resampling by looking at the area under the receiver operating characteristic curve. A perfect model would receive a score of 1, while a model that cannot predict the outcome will default to a score of 0.5. The extent to which something is a good score depends on the setting, but generally anything in the .8 to .9 range is very good while the .7 to .8 range is perfectly acceptable.
wflow_id | .metric | .estimator | .estimate |
GLM | roc_auc | binary | 0.84 |
Decision Tree | roc_auc | binary | 0.75 |
Another way to think about the model performance is to view its lift, or its ability to detect the positive outcomes over that of a null model. High lift indicates the model can much more quickly find all of the positive outcomes (in this case, games owned or played by the user), while a model with no lift is no better than random guessing. A gains chart is another way to view this.
While we are probably more interested in the lift provided by the models to evaluate their efficacy, we can also explore the optimal cutpoint if we wanted to define a hard threshold for identifying games a user will own vs not own.
The threshold we select depends on how we much we care about false positives (games the model predicts that the user does not own) vs false negatives (games the user owns that the model does not predict). We can toggle threshold to
Finally, we can understand the performance of the model by examining its calibration. If the model assigns a probability of 5%, how often does the outcome actually occur? A well calibrated model is one in which the predicted probabilities reflect the probabilities we would observe in the actual data. We can assess the calibration of a model by grouping its predictions into bins and assessing how often we observe the outcome versus how often our model expects to observe the outcome.
A model that is well calibrated will closely follow the dashed line - its expected probabilities match that of the observed probabilities. A model that consistently underestimates the probability of the event will be over this dashed line, be a while a model that overestimates the probability will be under the dashed line.
What games does the model think markbesada is most likely to own that are not in their collection?
Published | ID | Name | Pr(Owned) | Owned |
2015 | 175878 | 504 | 0.997 | no |
2019 | 286096 | Tapestry | 0.952 | no |
2016 | 200680 | Agricola (Revised Edition) | 0.929 | no |
2019 | 266507 | Clank!: Legacy – Acquisitions Incorporated | 0.895 | no |
2017 | 233078 | Twilight Imperium: Fourth Edition | 0.889 | no |
What games does the model think markbesada is least likely to own that are in their collection?
Published | ID | Name | Pr(Owned) | Owned |
1984 | 1815 | Spanish Main | 0.006 | yes |
1964 | 18755 | Pie Face | 0.006 | yes |
1972 | 8736 | Kasserine Pass | 0.008 | yes |
1983 | 12662 | To the Wolf's Lair! | 0.009 | yes |
2010 | 102219 | Connect 4 Launchers | 0.009 | yes |
Top 25 games most likely to be owned by the user in each year, highlighting in blue the games that the user has owned.
rank | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 |
1 | Space Cadets | Glass Road | Three Kingdoms Redux | 504 | Terraforming Mars | Gaia Project | Rising Sun | Maracaibo |
2 | Robinson Crusoe: Adventures on the Cursed Island | Cinque Terre | La Granja | GEM | Scythe | Gloomhaven | Newton | Tapestry |
3 | Terra Mystica | Lewis & Clark: The Expedition | Orléans | Blood Rage | Agricola (Revised Edition) | Spirit Island | Everdell | The Magnificent |
4 | Keyflower | Room 25 | AquaSphere | Mombasa | A Feast for Odin | BOX | Lords of Hellas | Era: Medieval Age |
5 | The Great Zimbabwe | Tash-Kalar: Arena of Legends | Fields of Arle | Mysterium | Hit Z Road | Twilight Imperium: Fourth Edition | TOKYO METRO | Clank!: Legacy – Acquisitions Incorporated |
6 | Escape: The Curse of the Temple | Concordia | Five Tribes | Food Chain Magnate | Great Western Trail | Wendake | Concordia Venus | Ragusa |
7 | Yedo | Patchistory | Imperial Settlers | SHH | Citadels | DIG | Mage Knight: Ultimate Edition | Hellenica: Story of Greece |
8 | CO₂ | 1862: Railway Mania in the Eastern Counties | Irish Gauge | The Pursuit of Happiness | Black Orchestra | RUM | CO₂: Second Chance | Pipeline |
9 | Archipelago | Impulse | Patchwork | The Gallerist | Captain Sonar | Clans of Caledonia | Pandemic: Fall of Rome | Barrage |
10 | Wallenstein (Second Edition) | Rococo | Splendor | SteamRollers | Codenames: Deep Undercover | Breaking Bad: The Board Game | Renegade | Clinic: Deluxe Edition |
11 | Il Vecchio | Bruxelles 1893 | Kill Shakespeare | LIE | Covert | GYM | Underwater Cities | Herbaceous Sprouts |
12 | Ginkgopolis | Legacy: The Testament of Duke de Crecy | Roll for the Galaxy | Grand Austria Hotel | The Castles of Burgundy: The Card Game | The Godfather: Corleone's Empire | Coimbra | Tiny Towns |
13 | Agricola: All Creatures Big and Small | Wildcatters | Deus | HUE | Star Wars: Rebellion | SPY | Founders of Gloomhaven | Sierra West |
14 | Suburbia | City of Remnants | Nations: The Dice Game | Through the Ages: A New Story of Civilization | Pandemic: Iberia | Lisboa | AuZtralia | Pandemic: Rapid Response |
15 | Descent: Journeys in the Dark (Second Edition) | Time 'n' Space | Arkwright | Codenames | Inis | SOW | Pax Emancipation | Watergate |
16 | Android: Netrunner | Gunrunners | Panamax | Porta Nigra | Forged in Steel | This War of Mine: The Board Game | Book of Dragons | Crystal Palace |
17 | New Amsterdam | BANG! The Dice Game | Roll Through the Ages: The Iron Age | Coffee Roaster | Explorers of the North Sea | Altiplano | New Frontiers | Aftermath |
18 | The Resistance: Avalon | Caverna: The Cave Farmers | Power Grid Deluxe: Europe/North America | TAJ | Flamme Rouge | Kitchen Rush | Railroad Ink: Deep Blue Edition | Rurik: Dawn of Kiev |
19 | Pax Porfiriana | Russian Railroads | Pandemic: The Cure | Haspelknecht: The Story of Early Coal Mining | Falling Sky: The Gallic Revolt Against Caesar | Sagrada | The Estates | Yukon Airways |
20 | Rex: Final Days of an Empire | Amerigo | Akrotiri | El Grande Big Box | Jórvík | Dinosaur Island | Cosmic Run: Regeneration | Bios: Origins (Second Edition) |
21 | Clash of Cultures | Ladies & Gentlemen | The Golden Ages | Pirates of the 7 Seas | Coal Baron: The Great Card Game | Nut | Architects of the West Kingdom | Black Angel |
22 | Serenissima (Second Edition) | Euphoria: Build a Better Dystopia | Black Fleet | Kraftwagen | Yokohama | Santa Maria | Forum Trajanum | Coloma |
23 | Milestones | Francis Drake | Deception: Murder in Hong Kong | Lancaster: Big Box | The Butterfly Garden | Pandemic Legacy: Season 2 | Brass: Birmingham | The Isle of Cats |
24 | Libertalia | Going, Going, GONE! | Praetor | BUS | Aeon's End | Carthago: Merchants & Guilds | The Pirate Republic | Amul |
25 | Snowdonia | Prosperity | Ultimate Werewolf | TKO | The Others | Calimala | Dice Settlers | Lux Aeterna |
This is an interactive table for the model’s predictions for the training set (from resampling).
We’ll validate the model by looking at its predictions for games published in 2020. That is, how well did a model trained on a user’s collection through 2020 perform in predicting games for the user in 2020?
username | outcome | dataset | method | .metric | .estimate |
markbesada | owned | validation | GLM | roc_auc | 0.729 |
markbesada | owned | validation | Decision Tree | roc_auc | 0.673 |
Table of top 50 games from 2020, highlighting games that the user owns.
Published | ID | Name | Pr(Owned) | Owned |
2020 | 184267 | On Mars | 0.976 | yes |
2020 | 229782 | Roland Wright: The Dice Game | 0.930 | no |
2020 | 291457 | Gloomhaven: Jaws of the Lion | 0.885 | yes |
2020 | 318983 | Faiyum | 0.849 | yes |
2020 | 300322 | Hallertau | 0.844 | yes |
2020 | 256317 | Guild Master | 0.842 | no |
2020 | 296151 | Viscounts of the West Kingdom | 0.772 | yes |
2020 | 306481 | Tawantinsuyu: The Inca Empire | 0.761 | yes |
2020 | 253506 | Versailles 1919 | 0.759 | yes |
2020 | 233262 | Tidal Blades: Heroes of the Reef | 0.737 | no |
2020 | 314040 | Pandemic Legacy: Season 0 | 0.712 | no |
2020 | 316554 | Dune: Imperium | 0.688 | no |
2020 | 308765 | Praga Caput Regni | 0.663 | yes |
2020 | 283155 | Calico | 0.644 | yes |
2020 | 304420 | Bonfire | 0.628 | no |
2020 | 292333 | Cowboys II: Cowboys & Indians Edition | 0.612 | no |
2020 | 284742 | Honey Buzz | 0.608 | yes |
2020 | 246900 | Eclipse: Second Dawn for the Galaxy | 0.607 | no |
2020 | 297486 | Ride the Rails | 0.595 | yes |
2020 | 279537 | The Search for Planet X | 0.574 | no |
2020 | 296626 | Sonora | 0.571 | yes |
2020 | 296100 | Rococo: Deluxe Edition | 0.568 | yes |
2020 | 286021 | Free Market: NYC | 0.568 | no |
2020 | 310442 | Feierabend | 0.565 | no |
2020 | 301880 | Raiders of Scythia | 0.561 | no |
2020 | 306040 | Merv: The Heart of the Silk Road | 0.552 | yes |
2020 | 316750 | The Princess Bride Adventure Book Game | 0.543 | no |
2020 | 299179 | Chancellorsville 1863 | 0.531 | no |
2020 | 295905 | Cosmic Frog | 0.512 | no |
2020 | 248125 | Monumental | 0.509 | no |
2020 | 300877 | New York Zoo | 0.506 | yes |
2020 | 284217 | Rush M.D. | 0.504 | yes |
2020 | 281466 | Yedo: Deluxe Master Set | 0.493 | no |
2020 | 306735 | Under Falling Skies | 0.478 | yes |
2020 | 295687 | Trust Me, I'm a Doctor | 0.471 | no |
2020 | 300327 | The Castles of Tuscany | 0.470 | yes |
2020 | 281655 | High Frontier 4 All | 0.467 | no |
2020 | 271055 | Dwellings of Eldervale | 0.466 | no |
2020 | 265784 | Cleopatra and the Society of Architects: Deluxe Edition | 0.459 | no |
2020 | 267009 | Rome & Roll | 0.458 | no |
2020 | 300001 | Renature | 0.449 | no |
2020 | 312251 | Curious Cargo | 0.448 | yes |
2020 | 295486 | My City | 0.446 | yes |
2020 | 253608 | 18Chesapeake | 0.443 | yes |
2020 | 316412 | The LOOP | 0.441 | no |
2020 | 284378 | Kanban EV | 0.437 | no |
2020 | 317985 | Beyond the Sun | 0.429 | no |
2020 | 276205 | Philosophia: Dare to be Wise | 0.422 | no |
2020 | 312804 | Pendulum | 0.403 | no |
2020 | 302425 | Unlock!: Mythic Adventures | 0.400 | no |
We can then refit our model to the training and validation set in order to predict all upcoming games for the user.
Examine the top 100 upcoming games, highlighting in blue ones the user already owns.
Rank | Published | ID | Name | Pr(Owned) | Owned |
1 | 2021 | 343905 | Boonlake | 0.986 | no |
2 | 2022 | 310873 | Carnegie | 0.935 | no |
3 | 2021 | 285967 | Ankh: Gods of Egypt | 0.916 | no |
4 | 2021 | 342942 | Ark Nova | 0.913 | no |
5 | 2021 | 344277 | Corrosion | 0.860 | no |
6 | 2022 | 341945 | La Granja: Deluxe Master Set | 0.848 | no |
7 | 2022 | 295374 | Long Shot: The Dice Game | 0.809 | yes |
8 | 2021 | 338760 | Imperial Steam | 0.808 | yes |
9 | 2022 | 295770 | Frosthaven | 0.781 | no |
10 | 2023 | 349793 | Age of Rome | 0.775 | no |
11 | 2022 | 317511 | Tindaya | 0.760 | no |
12 | 2022 | 319807 | Shogun no Katana | 0.745 | no |
13 | 2021 | 262941 | Dominant Species: Marine | 0.737 | yes |
14 | 2021 | 341048 | Free Ride | 0.727 | no |
15 | 2023 | 347909 | Rogue Angels: Legacy of the Burning Suns | 0.714 | no |
16 | 2021 | 325022 | Coffee Traders | 0.705 | yes |
17 | 2022 | 331106 | The Witcher: Old World | 0.703 | no |
18 | 2022 | 350316 | Wayfarers of the South Tigris | 0.702 | no |
19 | 2021 | 249277 | Brazil: Imperial | 0.691 | no |
20 | 2021 | 291572 | Oath: Chronicles of Empire and Exile | 0.691 | yes |
21 | 2022 | 240980 | Blood on the Clocktower | 0.690 | no |
22 | 2021 | 260524 | Beyond Humanity: Colonies | 0.667 | no |
23 | 2022 | 305096 | Endless Winter: Paleoamericans | 0.662 | no |
24 | 2021 | 292375 | The Great Wall | 0.659 | no |
25 | 2021 | 339484 | Savannah Park | 0.647 | no |
26 | 2021 | 326804 | Rorschach | 0.629 | no |
27 | 2022 | 283137 | Human Punishment: The Beginning | 0.625 | no |
28 | 2021 | 295535 | Dark Ages: Heritage of Charlemagne | 0.622 | no |
29 | 2021 | 304985 | Dark Ages: Holy Roman Empire | 0.605 | no |
30 | 2021 | 306202 | Philosophia: Floating World | 0.593 | no |
31 | 2022 | 326945 | Castles of Mad King Ludwig: Collector's Edition | 0.590 | no |
32 | 2022 | 266018 | Trinidad | 0.587 | no |
33 | 2022 | 311988 | Frostpunk: The Board Game | 0.568 | no |
34 | 2021 | 221298 | NewSpeak | 0.564 | no |
35 | 2021 | 283387 | Rocketmen | 0.558 | no |
36 | 2021 | 298102 | Roll Camera!: The Filmmaking Board Game | 0.554 | no |
37 | 2021 | 291859 | Riftforce | 0.552 | no |
38 | 2021 | 292899 | Tribune | 0.549 | no |
39 | 2022 | 338067 | 6: Siege – The Board Game | 0.541 | no |
40 | 2022 | 266064 | Trudvang Legends | 0.533 | no |
41 | 2022 | 322524 | Bardsung | 0.533 | no |
42 | 2021 | 252752 | Genotype: A Mendelian Genetics Game | 0.527 | no |
43 | 2021 | 325698 | Juicy Fruits | 0.526 | no |
44 | 2021 | 341169 | Great Western Trail (Second Edition) | 0.520 | no |
45 | 2022 | 280726 | Legacies | 0.520 | no |
46 | 2021 | 316287 | Quest | 0.518 | no |
47 | 2021 | 283242 | The Whatnot Cabinet | 0.517 | no |
48 | 2021 | 298378 | Maharaja | 0.514 | no |
49 | 2022 | 317321 | Darkest Dungeon: The Board Game | 0.512 | no |
50 | 2021 | 290236 | Canvas | 0.511 | no |
51 | 2021 | 339906 | The Hunger | 0.507 | no |
52 | 2021 | 301366 | Caves of Rwenzori | 0.499 | no |
53 | 2022 | 324894 | Free Radicals | 0.497 | no |
54 | 2021 | 340677 | Bad Company | 0.496 | no |
55 | 2021 | 333553 | For the King (and Me) | 0.492 | no |
56 | 2022 | 324090 | Scarface 1920 | 0.486 | no |
57 | 2021 | 323156 | Stroganov | 0.479 | no |
58 | 2022 | 330950 | Age of Galaxy | 0.477 | no |
59 | 2021 | 328871 | Terraforming Mars: Ares Expedition | 0.472 | yes |
60 | 2021 | 332944 | Sobek: 2 Players | 0.470 | no |
61 | 2022 | 352263 | Through Ice and Snow | 0.466 | no |
62 | 2021 | 309319 | Apogee | 0.462 | no |
63 | 2023 | 326538 | Small City: Deluxe Edition | 0.460 | no |
64 | 2021 | 344768 | Mobile Markets: A Smartphone Inc. Game | 0.459 | no |
65 | 2021 | 342848 | World of Warcraft: Wrath of the Lich King | 0.450 | yes |
66 | 2022 | 323707 | MOB: Big Apple | 0.448 | no |
67 | 2022 | 305462 | The Age of Atlantis | 0.441 | no |
68 | 2021 | 281248 | Cape May | 0.440 | no |
69 | 2022 | 273814 | Deliverance | 0.439 | no |
70 | 2021 | 320446 | Corduba 27 a.C. | 0.437 | no |
71 | 2022 | 352201 | Skull Canyon: Ski Fest | 0.435 | no |
72 | 2022 | 344105 | Anunnaki: Dawn of the Gods | 0.429 | no |
73 | 2022 | 322656 | burncycle | 0.428 | no |
74 | 2021 | 292126 | Excavation Earth | 0.426 | no |
75 | 2021 | 259962 | Stress Botics | 0.424 | no |
76 | 2021 | 317457 | Dinosaur World | 0.424 | no |
77 | 2021 | 300523 | Biblios: Quill and Parchment | 0.423 | yes |
78 | 2023 | 312959 | Rallyman: DIRT | 0.421 | no |
79 | 2021 | 315234 | Embarcadero | 0.421 | no |
80 | 2021 | 339789 | Welcome to the Moon | 0.418 | no |
81 | 2021 | 332386 | Brew | 0.416 | no |
82 | 2021 | 314491 | Meadow | 0.411 | no |
83 | 2022 | 314580 | Hamburg | 0.408 | no |
84 | 2021 | 259066 | Commands & Colors: Samurai Battles | 0.408 | no |
85 | 2021 | 331549 | MiniQuest Adventures | 0.403 | no |
86 | 2021 | 329591 | Ultimate Railroads | 0.399 | no |
87 | 2021 | 265635 | Space Race | 0.398 | no |
88 | 2021 | 343696 | Dune: Betrayal | 0.397 | no |
89 | 2021 | 238799 | Messina 1347 | 0.391 | no |
90 | 2021 | 338980 | Eastern Empires | 0.390 | no |
91 | 2021 | 303954 | Pax Viking | 0.389 | no |
92 | 2021 | 316786 | Tabannusi: Builders of Ur | 0.386 | no |
93 | 2022 | 271601 | Feed the Kraken | 0.382 | no |
94 | 2022 | 315610 | Massive Darkness 2: Hellscape | 0.380 | no |
95 | 2021 | 262201 | Sword & Sorcery: Ancient Chronicles | 0.379 | no |
96 | 2021 | 295947 | Cascadia | 0.378 | no |
97 | 2021 | 273330 | Bloodborne: The Board Game | 0.377 | no |
98 | 2021 | 308119 | Pax Renaissance: 2nd Edition | 0.373 | yes |
99 | 2021 | 328569 | Mint Bid | 0.371 | no |
100 | 2021 | 344258 | That Time You Killed Me | 0.366 | yes |